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Record W4307852197 · doi:10.18192/potentia.v13i.6341

REDD+ But Not Ready

2022· article· en· W4307852197 on OpenAlexafffundvenue
Sawyer Junger

Bibliographic record

VenuePotentia Journal of International Affairs · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Toronto
FundersUniversity of British ColumbiaGovernment of Canada
KeywordsAdditionalityCarbon offsetClean Development MechanismRatificationCarbon leakageGreenhouse gasCarbon marketCorporate governanceBusinessKyoto ProtocolEmissions tradingCarbon creditInternational economicsEconomicsEnvironmental economicsPolitical scienceFinanceEcology

Abstract

fetched live from OpenAlex

The ratification of Article 6 of the Paris Agreement allows countries to engage in compliance-based carbon markets which will allow countries to offset their own carbon emissions by investing in emissions reductions elsewhere. Given this recent development, it bears considering whether the international system is prepared to adopt existing voluntary carbon markets into a compliance system. Accordingly, this paper examines the nationally determined contributions (NDCs) of major REDD+ host countries to determine whether they are prepared to adopt this program under the compliance-based system. Given the well-documented risks of carbon offset markets to indirectly increase emission (via non-additionality, leakage, or incentivizing weak governance from host states), this paper argues that host states need to set clear guidelines towards the roles internal decarbonization policy and what is additional contributions from carbon markets. This paper ultimately argues that REDD+ is ill-equipped to function under a carbon market as host states appear reliant on REDD+ to achieve their own internal climate goals and suggests that better accountability is needed for REDD+ to be adopted under article 6.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.246
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes3
Has abstractyes

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